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29 Aug 2021
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Improving human collective decision-making through animal and artificial intelligence

Bio-inspired solutions for collective decision-making in a networked society 

Recommended by based on reviews by Camelia Florela Voinea and 1 anonymous reviewer

Formal voting biases and weaknesses like Arrow's or Condorcet's paradoxes are known for quite a long time, but they are often considered as curiosities for mathematicians and cases that rarely occur in practice. Although, recent elections underlined the actual weaknesses of our collective decision-making processes, either concerning the resulting lack of representativity (people getting elected but representing actually a minority of the population) or more globally the efficiency of the decisions taken. A significant gap exists in between on one hand the opinions of a networked population that are structured and evolve using social media, leading to a much more visible diversity and on the other hand the ones of representatives that are still structured by political parties. Such a situation leads to questioning the processes used to choose representatives and more globally to make collective decision-making with alternatives that are proposed more and more (liquid democracy for instance (Blum and Zuber, 2016).

The article by Sueur et al. shed new light on the situation by proposing to examine further the solutions selected along time by the animal kingdom in order to make collective decision-making. Although they advocate that the decisions taken are not the same (decision for an animal group to move to another place) and do not involve the same cognitive abilities at the individual level, the existing processes could get adapted to human contexts. One of the most striking advantages of such bio-inspired approach is that animal collective decision-making processes are robust and enable to manage conflicting views, diversity of opinions and avoid forms of despotism that are not much present in animals, rather consensual decision-making being the norm. Another argument, probably less put forward by the authors is that such solutions may scale well with large networked populations as they exist for some animal species. Therefore it looks like we could have a kind of readymade library of processes for collective decision-making that are yet efficient to make timely decisions for different purposes with different population sizes and structures.

Their argument is consolidated by the possibility of using AI technologies in order to enable to support the adaptation of such solutions. Without building explicitly the link, they identify that AI could be used in order to guarantee a fair process and to scale up the proposed solutions at the level of massive populations.

This is probably the less convincing part of the proposal and it concerns the relation between human and AI. Even if the authors admit that the acceptance of AI solutions by part of the population is a key issue, as it concerns directly the legitimacy of the process and the compliance of the population with the resulting decision. They tend to minimize the gap to fill before having a fair AI with potential behavior that can be verified before using it with confidence to support a democratic process of decision-making. 

Nevertheless, the article brings forward good arguments, well formalized using relevant concepts for the use of bio-inspired solutions for collective decision-making.

References

Blum C, Zuber CI (2016) Liquid Democracy: Potentials, Problems, and Perspectives. Journal of Political Philosophy, 24, 162–182. https://doi.org/10.1111/jopp.12065

Sueur C, Bousquet C, Espinosa R, Deneubourg J-L (2021) Improving human collective decision-making through animal and artificial intelligence. hal-03299087, ver. 3 recommended and peer-reviewed by Peer Community in Network Science. https://hal.archives-ouvertes.fr/hal-03299087

Improving human collective decision-making through animal and artificial intelligenceCédric Sueur, Christophe Bousquet, Romain Espinosa and Jean-Louis Deneubourg<p style="text-align: justify;">Whilst fundamental to human societies, collective decision-making such as voting systems can lead to non-efficient decisions, as past climate policies demonstrate. Current systems are harshly criticized for the way ...Animal networks, Political networksFrédéric Amblard2021-04-19 07:10:05 View
20 Sep 2023
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Structify-Net: Random Graph generation with controlled size and customized structure

A model petting zoo for interacting with network structure

Recommended by based on reviews by 2 anonymous reviewers

If you work, study or play in network science then chances are you have generated a network. Whether or not you have a real-world system to analyse, synthetic networks play an important role in network science. Generating networks of a chosen size can provide a null model for a statistical test, a test bed for new algorithms or the basis for studying the interplay between structure and dynamics in complex systems. Consequently network science literature contains a wide array of network models: some designed as processes to replicate observed properties and others for the purposes of statistical inference. However, these models have different parameters and constraints associated with their generative models, may or may not have the ability to control for random noise and do not always have readily available software implementations, thus making them unavailable to network science practitioners.

The article of Cazabet et al. (2023) introduces a software "zoo, " called Structify-Net, that contains a range of models that the authors have captured from the wild. The authors have focused on developing a framework that enables the generation networks of a chosen size, according to number of nodes and edges, and provides the means to control for randomness, by interpolating between the specified structure and a random graph. The article also discusses an interesting use case to examine the interplay between network structure and node attributes, which might compliment methods based on permutation tests (Bianconi et al. 2009, Ehrhardt and Wolfe 2019). 

Structify-Net presents some interesting future opportunities. For instance, the independence that Structify-Net imposes on edge ranking (defined by the model) and the expected number of edges (defined by the user) might offer a route towards exploring network growth or evolution. Like any zoo Structify-Net is not complete in that there are many more exotic "species" that the authors, or perhaps others in the network science community, may later collect. Collecting more model implementations to align with reviews of network models (Goldenberg et al. 2010) together with methods of statistical inference has the potential to lay the foundations for the ever important bridge between theory and practice in network science (Peel et al. 2022).

References

Bianconi, Ginestra, Paolo Pin, and Matteo Marsili (2009) Assessing the Relevance of Node Features for Network Structure. Proceedings of the National Academy of Sciences 106, 28: 11433–38. https://doi.org/10.1073/pnas.0811511106

Cazabet, Remy, Salvatore Citraro, and Giulio Rossetti (2023) Structify-Net: Random Graph Generation with Controlled Size and Customized Structure. arXiv, ver. 2 peer-reviewed and recommended by Peer Community in Network Science. https://doi.org/10.48550/arXiv.2306.05274

Ehrhardt, Beate, and Patrick J. Wolfe (2019) Network Modularity in the Presence of Covariates’. SIAM Review 61, 2: 261–76. https://doi.org/10.1137/17M1111528

Goldenberg, Anna, Alice X. Zheng, Stephen E. Fienberg, and Edoardo M. Airoldi (2010) A Survey of Statistical Network Models. Foundations and Trends in Machine Learning 2, 2: 129–233. https://doi.org/10.1561/2200000005

Peel, Leto, Tiago P. Peixoto, and Manlio De Domenico (2022) Statistical Inference Links Data and Theory in Network Science. Nature Communications 13, 1: 6794. https://doi.org/10.1038/s41467-022-34267-9

Structify-Net: Random Graph generation with controlled size and customized structureRemy Cazabet, Salvatore Citraro, Giulio Rossetti<p>Network structure is often considered one of the most important features of a network, and various models exist to generate graphs having one of the most studied types of structures, such as blocks/communities or spatial structures. In this art...Algorithms for Network Analysis, Clustering in networks, Community structure in networks, Geometry and topology of networks or graphs, Graph models, Network models, Random graphs, Spatial networks, Structural network propertiesLeto PeelAnonymous, Anonymous2023-06-09 10:41:32 View